Data were processed using nf-core rnaseq pipeline revision 3.14.0 using RSEM/STAR for abundance estimation against Homo Sapiens GRCh38 release-86.
| sample_id | sample_name | condition | condition_name | plate_id |
|---|---|---|---|---|
| ING7705A1 | C_1 | C | control | 1 |
| ING7705A2 | DN_1 | DN | SOX17neg_FOXC1neg | 1 |
| ING7705A3 | DP_1 | DP | SOX17pos_FOXC1pos | 1 |
| ING7705A4 | A_1 | A | SOX17neg_FOXC1pos | 1 |
| ING7705A5 | S17_1 | S17 | SOX17pos_FOXC1neg | 1 |
| ING7705A6 | C_2 | C | control | 2 |
| ING7705A7 | DN_2 | DN | SOX17neg_FOXC1neg | 2 |
| ING7705A8 | DP_2 | DP | SOX17pos_FOXC1pos | 2 |
| ING7705A9 | A_2 | A | SOX17neg_FOXC1pos | 2 |
| ING7705A10 | S17_2 | S17 | SOX17pos_FOXC1neg | 2 |
| ING7705A11 | C_3 | C | control | 3 |
| ING7705A12 | DN_3 | DN | SOX17neg_FOXC1neg | 3 |
| ING7705A13 | DP_3 | DP | SOX17pos_FOXC1pos | 3 |
| ING7705A14 | A_3 | A | SOX17neg_FOXC1pos | 3 |
| ING7705A15 | S17_3 | S17 | SOX17pos_FOXC1neg | 3 |
Raw read counts for each gene per sample
Normalized counts using size factors.
Expressed genes are defined as those that have count > 0 in at least 1 sample.
Heatmap of samples distances to assess overall similarities and dissimilarities between samples. Sample similarity is assessed using a Poisson dissimilarity metric, which is fairly robust to differences in library sizes between samples.
Principal Component Analysis (PCA) based on the variance stabilised abundance estimates. It uses all available genes. The majority of the variance in the dataset is shown in the two PC and associated with a time effect.
Heatmaps of the top most variable genes across all samples. The top 500 genes based on a coefficient of variation are displayed (sd/mean). Note that don’t necessarily relate to genes changing significantly between replicate conditions. These visualisations are carried out blind to the experimental design. We’d expect samples from the same experimental group to cluster together.
\[ design = plate_id + condition \]
Pairwise differential expression between condition groups was assessed using a DESeq2’s Wald test. Significance was assessed based on an independent hypothesis weighting (IHW) value of < 0.05, together with a minimum fold-change of > 0 and a minimum baseMean (i.e. mean abundance across all samples) of > 5. Note that log2 fold changes were shrunk using the “ashr” method prior to filtering.
Thresholds applied:
| comparison | up | down | total |
|---|---|---|---|
| DN_vs_C | 2765 | 2687 | 5452 |
| DP_vs_C | 4644 | 4419 | 9063 |
| A_vs_C | 4499 | 4229 | 8728 |
| S17_vs_C | 4599 | 4216 | 8815 |
| DP_vs_DN | 3573 | 3529 | 7102 |
| A_vs_DN | 3392 | 3346 | 6738 |
| S17_vs_DN | 3196 | 2662 | 5858 |
| DP_vs_A | 1864 | 1836 | 3700 |
| DP_vs_S17 | 1448 | 1833 | 3281 |
| S17_vs_A | 2572 | 2113 | 4685 |
Assess the overlap of differential genes between the various comparisons. Note that this is agnostic of direction of change.
Heatmaps of the top 30 most up-regulated and top 30 most down-regulated differentially expressed genes per comparison. If fewer than 30 genes are called differential in either direction, then only those are displayed. If there are no differential genes then the heatmap is skipped.
Heatmap derived from a combined view of all differentially expressed genes from all available tests (13680 genes).
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
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## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
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## time zone: Etc/UTC
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## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
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## other attached packages:
## [1] writexl_1.5.2 openxlsx_4.2.8
## [3] kableExtra_1.4.0 clusterProfiler_4.14.6
## [5] PoiClaClu_1.0.2.1 PCAtools_2.18.0
## [7] ggrepel_0.9.6 ashr_2.2-63
## [9] IHW_1.34.0 edgeR_4.4.2
## [11] limma_3.62.2 DESeq2_1.46.0
## [13] SummarizedExperiment_1.36.0 MatrixGenerics_1.18.1
## [15] matrixStats_1.5.0 tximport_1.34.0
## [17] org.Hs.eg.db_3.20.0 UpSetR_1.4.0
## [19] GenomicFeatures_1.58.0 AnnotationDbi_1.68.0
## [21] Biobase_2.66.0 BiocParallel_1.40.0
## [23] scales_1.3.0 reshape2_1.4.4
## [25] viridis_0.6.5 viridisLite_0.4.2
## [27] pheatmap_1.0.12 circlize_0.4.16
## [29] ComplexHeatmap_2.22.0 RColorBrewer_1.1-3
## [31] plyranges_1.26.0 GenomicRanges_1.58.0
## [33] GenomeInfoDb_1.42.3 IRanges_2.40.1
## [35] S4Vectors_0.44.0 BiocGenerics_0.52.0
## [37] lubridate_1.9.4 forcats_1.0.0
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## [45] ggplot2_3.5.1 tidyverse_2.0.0
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## loaded via a namespace (and not attached):
## [1] splines_4.4.1 BiocIO_1.16.0
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